OpenAI's In-House AI Chip Nears Production: Breaking Free from Nvidia's Grip

OpenAI's In-House AI Chip Nears Production: Breaking Free from Nvidia's GripNews broke on February 11th that OpenAI is aggressively pursuing its plan to reduce its reliance on Nvidia chips, and has entered a crucial phase. Sources reveal that the design of OpenAI's first self-developed AI chip will be completed in the coming months, with plans to submit it to TSMC for "tape-out," meaning trial production

OpenAI's In-House AI Chip Nears Production: Breaking Free from Nvidia's Grip

News broke on February 11th that OpenAI is aggressively pursuing its plan to reduce its reliance on Nvidia chips, and has entered a crucial phase. Sources reveal that the design of OpenAI's first self-developed AI chip will be completed in the coming months, with plans to submit it to TSMC for "tape-out," meaning trial production. While both OpenAI and TSMC declined to comment, this news suggests OpenAI is on track to meet its goal of large-scale production of its self-designed chip at TSMC by 2026.

The tape-out process typically costs tens of millions of dollars and takes about six months to produce finished chips, unless OpenAI pays extra to expedite the process. It's crucial to note that the first tape-out doesn't guarantee a perfectly functioning chip. If problems arise, OpenAI will need to diagnose, fix them, and repeat the tape-out process. The entire process is fraught with challenges and uncertainties, requiring strong technical capabilities and ample financial backing.

This training-focused chip is considered a strategic move within OpenAI. It's more than just a chip; it's a crucial tool for strengthening OpenAI's negotiating power, giving it greater leverage in discussions with chip suppliers like Nvidia. After the first chip goes into production, OpenAI engineers plan to gradually develop more powerful and versatile subsequent processors, continuously enhancing its competitiveness in the AI field.

If the initial tape-out is successful, OpenAI will be able to mass-produce its first self-developed AI chip and test its feasibility as an Nvidia chip replacement later this year. OpenAI's plan to submit the design to TSMC this year is remarkably fast, as other chip design companies typically take several years to complete a similar design process. This rapid progress highlights OpenAI's speed in AI chip R&D, in contrast to the less satisfactory results achieved by tech giants like Microsoft and Meta after years of effort in chip production.

The recent market disruption caused by the Chinese AI startup DeepSeek has also raised questions about the number of chips needed to develop powerful AI models. The DeepSeek incident might suggest that future development of powerful AI models may not require such a massive number of chips, providing OpenAI and other companies with new avenues for thought in chip R&D.

The chip is designed by an internal OpenAI team led by Richard Ho. This team has rapidly expanded to 40 people in recent months and is collaborating with Broadcom. Ho joined OpenAI over a year ago from Google, where he previously led a self-developed AI chip project. Compared to the massive chip R&D teams of tech giants like Google and Amazon, OpenAI's design team is relatively small, but its efficiency is impressive.

Industry insiders familiar with chip design budgets reveal that developing a chip with mass-market potential could cost as much as $500 million. Adding the development of necessary software and peripherals could double the overall cost, representing a massive investment. Generative AI model developers like OpenAI, Google, and Meta have demonstrated that interconnecting numerous chips in data centers significantly improves model intelligence, resulting in a seemingly endless demand for chips. Meta previously announced a $60 billion investment in AI infrastructure over the next year, while Microsoft plans to invest $80 billion by 2025, illustrating the enormous scale of the AI chip market.

Currently, Nvidia holds approximately 80% of the market share, making its chips the most popular choice in the AI field. OpenAI also participated in the $500 billion "Stargate" AI infrastructure plan announced last month by President Trump (correction from original text which incorrectly stated Biden). However, with rising costs and increasing reliance on a single supplier, major clients like Microsoft, Meta, and OpenAI are actively exploring in-house or external alternatives to reduce their dependence on Nvidia chips, seeking more robust supply chains and more cost-effective solutions.

Sources say that while OpenAI's self-developed AI chip has the capability to both train and run AI models, it will initially be primarily used for running AI models and deployed on a limited scale. If OpenAI wants to build a large-scale AI chip project like Google or Amazon's, it would need to hire hundreds of engineers to build a much larger R&D team.

TSMC will manufacture OpenAI's AI chip using its advanced 3-nanometer process technology. Sources indicate the chip utilizes a common system-on-a-chip architecture, features high-bandwidth memory (HBM) similar to Nvidia's chips, and has extensive networking capabilities. These technical details show OpenAI's deep technical expertise in chip design and its commitment to creating a high-performance AI chip. This move by OpenAI not only concerns its own future development but also signals a more intense competition in the AI chip field, driving technological advancements and innovation across the industry. Breaking free from reliance on a single supplier will give OpenAI a stronger competitive advantage in the fierce AI race and lay a solid foundation for its long-term development. The success or failure of this chip will not only test OpenAI's technical prowess but also explore the future direction of the entire AI industry.